Process for predicting the prognosis of squamous cell lung cancer

ABSTRACT

Disclosed in this specification is a method for predicting the prognosis of squamous cell lung cancer by observing regulatory changes in select miRNA sequences. These sequences may include hsa-mir-146b, hsa-mir-191, hsa-mir-206, hsa-mir-299-3p, hsa-mir-155, hsa-mir-15a, hsa-mir-122a, hsa-mir-513, hsa-mir-184, hsa-mir-511, hsa-mir-100, hsa-mir-10a, hsa-mir-453, hsa-mir-379, hsa-mir-202, hsa-mir-21, hsa-mir-126, hsa-mir-494, hsa-mir-432, hsa-mir-370, and combinations of these sequences.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of co-pending U.S. patentapplication Ser. No. 12/261,825, filed Dec. 15, 2010, which claimspriority to and the benefit of U.S. Provisional Patent Application No.60/983,756, filed Oct. 30, 2007, both of which are hereby incorporatedby reference in their entireties.

REFERENCE TO A SEQUENCE LISTING, A TABLE, OR A PROGRAM LISTING

This application refers to a “Sequence Listing” listed below, which isprovided as an electronic document entitled “Sequence listing.txt” (6kb, created on Oct. 29, 2008), which is incorporated herein by referencein its entirety.

FIELD OF THE INVENTION

This invention relates, in one embodiment, to a method for providing aprognosis of squamous cell lung cancer by observing regulatory changesin the production of select microRNA (miRNA) sequences. By observing upregulation or down regulation of specified sequences, both the presenceof cancer cells as well as the prognosis of cancer may be determined.

BACKGROUND OF THE INVENTION

Lung cancer is the most common cause of cancer related deaths worldwidewhile non-small-cell lung cancers (NSCLC) represent the most frequenttype of broncogenic carcinomas. NSCLC is the cause of 80% of all lungcancer deaths in the United States and is composed primarily of,adenocarcinoma, and squamous cell carcinoma (SSC), and to a lesserextent large-cell cancer. Despite potentially curative surgeryapproximately 40% of patients will relapse within 5 years. Genomicprofiling of NSCLC has recently provided insight into predicting theprognosis of patients with this disease. These genomic classifiers cancontain up to several hundred genes for the identification of patientswith early stage NSCLC who might benefit from chemotherapy in additionto surgical resection.

SUMMARY OF THE INVENTION

The invention comprises, in one form thereof, a method for detecting thepresence of squamous cell lung cancer in a cell sample. Applicants havediscovered certain miRNAs that are differentially regulated in squamouscell lung cancers relative to wild type cells. By determining the degreeof regulatory changes in such miRNAs, one can determine if a tissuesample includes squamous cell lung cancer cells.

In another form the invention is a method for predicting the prognosisof squamous cell lung cancer. Applicants have discovered certain othermiRNAs that enable the prognosis to be predicted for a patient withsquamous cell lung cancer. By monitoring these miRNAs, a more accurateprognosis may be provided.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is disclosed with reference to the accompanyingdrawings, wherein:

FIG. 1A is a graph that correlates the number of miRNA sequences ofinterest to predictive prognosis;

FIG. 1B is graph that correlates survival percentage to time for twodiffering level of miR-146b expression.

Corresponding reference characters indicate corresponding partsthroughout the several views. The examples set out herein illustrateseveral embodiments of the invention but should not be construed aslimiting the scope of the invention in any manner.

DETAILED DESCRIPTION Definitions

The phrase “regulation change” refers to a change in the abundance of acellular component, such a miRNA, relative to the abundance of the samecellular component in a wild type cell. The phrase “down regulation”refers to a decrease in the abundance of the cellular component inquestion while the phrase “up regulation” refers to an increase in theabundance of the component.

Identification of Squamous Cell Lung Cancer by Differential miRNARegulation

Squamous cell lung cancer tissue was compared to wild type tissue andfifteen differentially expressed miRNAs were identified (Table 1).Tissue samples included both cell lines and clinical samples. Total RNAwas extracted from the cell samples in accordance with conventionaltechniques. For example, mirVana isolation kit (Ambion) for snap-frozensamples and the RecoverAll™ Total Nucleic Acid Isolation Kit forformalin-fixed, paraffin-embedded (FFPE) samples (Ambion) may be used.Other conventional RNA extraction methods may also be used. Once thetotal RNA is extracted, small (less than forty nucleotides) RNA may beisolated by gel electrophoresis. The samples were analyzed to determinethe identity and abundance of specific miRNA sequences. Any suitabletechnique may be used to determine the identity and abundance such as,but not limited to commercially available miRNA kits, such as mirVanaBioarray (Ambion). Fifteen differentially expressed miRNAs wereidentified in squamous cell lung cancer cells which had significantlyaltered expression relative to a wild type sample. These sequences areshown in Table 1.

TABLE 1 miRNAs differentially expressed between normal lung and lungsquamous cell carcinoma. Fold mean mean SEQ ID Name Change LN LC Dir SEQID NO. 1 hsa-mir-210 3.25 6.0 7.7 up SEQ ID NO. 2 hsa-mir-200c 2.46 9.510.8 up SEQ ID NO. 3 hsa-mir-17-5p 2.14 6.4 7.5 up SEQ ID NO. 4hsa-mir-20a 1.41 5.6 6.1 up SEQ ID NO. 5 hsa-mir-125a 0.41 10.20 8.9down SEQ ID NO. 6 hsa-let-7e 0.76 9.5 9.1 down SEQ ID NO. 7 hsa-mir-200a2.46 5.7 7.0 up SEQ ID NO. 8 hsa-mir-106b 2.00 5.9 6.9 up SEQ ID NO. 9hsa-mir-93 2.46 7.5 8.8 up SEQ ID NO. 10 hsa-mir-182 2.30 5.6 6.8 up SEQID NO. 11 hsa-mir-183 1.62 5.6 6.3 up SEQ ID NO. 12 hsa-mir-106a 2.306.5 7.7 up SEQ ID NO. 13 hsa-mir-20b 1.87 5.2 6.1 up SEQ ID NO. 14hsa-mir-224 2.64 5.5 6.9 up LN = lung normal signal intensity (log2); LC= lung cancer signal intensity (log2) Fold Change = 2^((LC−LN))

In one embodiment of the invention, a sample is analyzed to determinethe abundance of a specified miRNA sequence from Table 1. The sample maybe a tissue sample, such as a sample obtained during a surgicalprocedure. Alternatively, the sample may be obtained non-invasivelyfrom, for example, a blood sample or from a similar source. Theabundance of a specified miRNA in the sample is determined and aregulation change is observed relative to an average sample. SincemiRNAs are known to persist outside of the cell, free miRNAs can providea screening method for squamous cell lung cancer. Such screening can beperformed using non-invasive sampling techniques, such as a simple bloodtest. In this fashion patients in a high-risk group could be routinelytested to help identify the early development of cancer.

In another embodiment, miRNA abundances are observed to distinguishsquamous cell lung cancer from adenocarcinoma. By observing regulatorychanges in miRNA expression, such a distinction can be made even whentissue morphology is unclear.

Prognosis of Lung Squamous Cell Carcinoma

Additional miRNA sequences have been discovered that permit one topredict the prognosis of lung squamous cell carcinoma. Twenty miRNAsequences were so identified as being tightly associated with squamouscell lung carcinoma prognosis. Clinical samples were collected betweenOctober 1991 and July 2002. The medical history of each of the patentswas also collected and a correlation was made between those miRNAs thatexpressed altered regulations and the patients prognosis. The details ofthis correlation process are discussed elsewhere in this specification.In this manner, those miRNA sequences which strongly influence patientprognosis were identified. The identified sequences are listed in Table2.

TABLE 2 miRNAs associated with squamous cell lung carcinoma prognosis.Fold SEQ ID Name Change mean_0 mean_1 Dir SEQ ID NO. 15 hsa-mir-146b1.51 6.97 7.57 up SEQ ID NO. 16 hsa-mir-191 1.23 8.53 8.83 up SEQ ID NO.17 hsa-mir-206 0.82 6.13 5.84 down SEQ ID NO. 18 hsa-mir-299- 0.82 6.195.90 down 3p SEQ ID NO. 19 hsa-mir-155 1.33 7.17 7.58 up SEQ ID NO. 20hsa-mir-15a 1.21 6.24 6.51 up SEQ ID NO. 21 hsa-mir-122a 0.74 6.86 6.42down SEQ ID NO. 22 hsa-mir-513 0.8 6.99 6.67 down SEQ ID NO. 23hsa-mir-184 0.71 6.72 6.24 down SEQ ID NO. 24 hsa-mir-511 1.23 6.56 6.85up SEQ ID NO. 25 hsa-mir-100 1.27 7.15 7.50 up SEQ ID NO. 26 hsa-mir-10a1.24 6.45 6.77 up SEQ ID NO. 27 hsa-mir-453 0.74 7.18 6.75 down SEQ IDNO. 28 hsa-mir-379 0.78 6.68 6.32 down SEQ ID NO. 29 hsa-mir-202 0.627.89 7.20 down SEQ ID NO. 30 hsa-mir-21 1.41 9.32 9.81 up SEQ ID NO. 31hsa-mir-126 1.27 7.60 7.94 up SEQ ID NO. 32 hsa-mir-494 0.63 10.53 9.87down SEQ ID NO. 33 hsa-mir-432 0.67 7.97 7.40 down SEQ ID NO. 34hsa-mir-370 0.79 7.25 6.90 down Mean_0 and Mean_1 are wild type andcancerous tissues, respectively.

Using a five-fold cross validation, it was found that the highest meanvalue for predicting overall survival within three years was 78% whenusing miR-146b alone (SEQ ID NO. 15). When three or more additionalmiRNAs were added to Mir-146b in a linear fashion, the predictiveaccuracy dropped by approximately 68%, but thereafter stabilized. SeeFIG. 1A. Patients with high miR-146 up regulation had significantlyworse overall survival (26 months) compared to the low miRNA-146b group(95 months). See FIG. 1B. In FIG. 1B, a “high” miRNA-146 group (thelower of the two lines) was defined as those with miRNA-146 levels abovethe median value. The “low” miRNA-146 group (the higher of the twolines) was defined as those with miRNA-146 below the median value.

By measuring the abundance of specified miRNA sequences a predictedprognosis may be provided to a patient. For example, statistical datamay be gathered that correlates miRNA abundance of a specific sequenceto patent survivability as a function of time. For example, for apatient with a large regulatory change in a certain miRNA sequence, thedata may indicate that remission is 78% likely within the next threeyears. This predicted prognosis may be provided to the patient. FIG. 1Bprovides sample data for miR-146b, but such data should not be construedas limiting. Additional data, such as the demographic information of thepatient, may be taken into consideration such broader statisticalinformation is gathered.

The miRNA may be extracted from a tissue sample using conventionaltechniques. Such a sample may be obtained during a surgical procedure.Alternatively, miRNA may be isolated from a non-tissue sample. Forexample, miRNA may be isolated from a blood, stool, urine or otherbiological sample. The abundance of the specific miRNA found in thesample is compared to a normal sample. Up regulated or down regulatedmiRNA abundances may be indicative of a cancer.

The abundance of the specified miRNA sequence(s) may be determined inaccordance with any known technique. By way of illustration, but notlimitation, QPCR may be used.

The miRNA sequences in the attached sequence listing represent commonlyisolated miRNA sequences. Alterations at the termini of the listedsequences are known in the art and fall within the scope of theinvention provided that the residues are at least 95% homologous.

While the invention has been described with reference to specificembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof to adapt to particular situations without departingfrom the scope of the invention. Therefore, it is intended that theinvention not be limited to the particular embodiments disclosed or theparticular mode contemplated for carrying out this invention, but thatthe invention will include all embodiments falling within the scope andspirit of the appended claims.

Methods Clinical Samples

In total, sixty-one snap-frozen lung SCC and 10 matched normal adjacentlung tissue samples were evaluated for miRNA expression. These sampleswere collected from patients from the University of Michigan Hospitalbetween October 1991 and July 2002 with patient consent andInstitutional Review Board approval. Samples chosen for analysiscontained greater than 70% tumor cells. Of the sixty-one tumor samplesfifty-seven had sufficient follow up clinical information and were usedfor prognostic analysis. Fifty-four of the fifty-seven were previouslyprofiled using the Affymetrix U133A GeneChip (GSE4573).

Ambion miRNA Expression Profiling

The mirVana Bioarray (Ambion, version 2) that contains 328 human miRNAprobes was employed to identify lung SCC miRNA signatures. Total RNA wasisolated using Trizol. MiRNA was isolated from 4 ug of total RNA usingthe mirVana isolation kit (Ambion). All samples were then fractionatedby polyacrylamide gel electrophoresis (Flash-Page Ambion) and small RNAs(<40 nt) were recovered by ethanol precipitation with linear acrylamide.Quantitative RT-PCR (qPCR) of miR-16 was used to confirm miRNAenrichment prior to miRNA array analysis. If the Ct value of miR-16 wasgreater than 25 then the miRNA isolation was considered a failure.

The small RNA samples were subject to poly(A) polymerase reactionwherein amine modified uridines were incorporated (Ambion). The tailedsamples were then fluorescently labeled using the amine-reactive Cy3(Invitrogen). The aforementioned labeling technique is only one possiblemethodology. Other suitable labeling techniques would be apparent tothose skilled in the art after benefiting from reading thisspecification. Such alternative methods are contemplated for use withthe instant specification. The fluorescently labeled RNAs were purifiedusing a glass-fiber filter and eluted (Ambion). Each sample was thenhybridized to the Bioarray slides for 14 hours at 42° C. (Ambion). Thearrays were washed and scanned using an Agilent 2505B confocol lasermicroarray scanner (Agilent) and data was obtained using the ExpressionAnalysis software (Codelink, version 4.2).

miRNA Quantitative PCR

Quantitative PCR (QPCR) was performed using the ABI miRNA Taqmanreagents to verify miRNA expression profiles. Ten ng of total RNA wasconverted to cDNA using the High Capacity DNA Archive kit and 3 ul of 5×RT primer according to the manufacturer's instructions (Ambion). The 15μl reactions were incubated in a thermocycler for 30 min at 16° C., 30min at 42° C., 5 min at 85° C. and held at 4° C. All reversetranscriptase reactions included no template controls. QPCR wasperformed using a standard Taqman® PCR kit protocol on an AppliedBiosystems 7900HT Sequence Detection System. The 10 μl PCR reactionincluded 0.66 μl RT product, 1 μl Taqman miRNA assay primer and probemix, 5 μl Taqman 2× Universal PCR master mix (No Amperase UNG) and 3.34μl water. The reactions were incubated in a 384 well plate at 95° C. for10 min, followed by 40 cycles of 95° C. for 15 sec, and 60° C. for 2min. All QPCR reactions included a no cDNA control and all reactionswere performed in triplicate.

MiRNA Statistical Analyses

Probes flagged by the Expression Analysis software were first removedand background median values were subtracted from the spot mean. Outliersamples were identified and removed if the number of flagged probes wasless than the mean minus the standard deviation of flagged probes perchip. Spot intensity values below zero were set to 0.5 and the data wasthen quantile normalized. A background cutoff of 6 (log2 normalized) wasidentified by plotting the correlations of replicate probes across allsamples versus the median of median intensities. Therefore, miRNAs wereremoved from further analyses if the normalized signal intensity wasless than 6 in either comparison group. This cutoff was chosen since thecorrelation of replicate probes dropped precipitously below this value.

Survival analysis was performed using the Significance of MicroarrayAnalysis (SAM) algorithm (Tusher VG, Tibshirani R, Chu G. Significanceanalysis of microarrays applied to the ionizing radiation response. ProcNatl Acad Sci USA 2001; 98(9):5116-21). MiRNAs were selected as beingsignificantly associated with overall survival if the paired t-testp-value was less than 0.05 and the area under the curve (AUC) from areceiver operator characteristic analysis was greater than 0.65 using a3-year cut-off. A maximum 3 years follow-up was employed since themajority of patients who will relapse in this population will do sowithin 3 years. Also many of these patients were aged and death due tonon-cancer related illnesses would likely increase after 3 years. Todetermine the minimum number of miRNAs used to construct a prognosticclassifier, combinations of gene expression markers were tested byadding one gene at a time according to the rank order. For eachsignature with increasing number of genes, Receiver OperatingCharacteristic (ROC) analysis using death within 3 years as the definingpoint was performed, in 100 5-fold cross validations, to calculate theaverage area under the curve (AUC).

1. A process for predicting the prognosis of squamous cell lungcarcinoma in humans comprising the steps of: Determining determining theexpression of microRNA having SEQ ID NO. 16; and predicting a prognosisof squamous cell lung carcinoma if the expression exceeds apredetermined cutoff.
 2. The process as recited in claim 1, furthercomprising the step of removing RNA with more than forty nucleotidesprior to the step of observing a regulation change.
 3. The process asrecited in claim 1, wherein an up regulation is observed.
 4. A processfor predicting the prognosis of squamous cell lung carcinoma in humanscomprising the steps of: observing a regulation change of SEQ ID NO. 16within extracted RNA relative to the same microRNA in a wild type lungtissue sample; predicting a prognosis of squamous cell lung carcinomabased on the observation.
 5. The process as recited in claim 4, furthercomprising the step of extracting total RNA from a sample of lungtissue.
 6. The process as recited in claim 5, wherein the step ofobserving includes the step of performing a quantitative polymerasechain reaction.
 7. A process for predicting the prognosis of squamouscell lung carcinoma in humans comprising the steps of: observing theabundance of SEQ ID NO. 16 within a sample; predicting a prognosis ofsquamous cell lung carcinoma based on the observation.
 8. The process asrecited in claim 7, wherein the step of observing the abundance includesthe step of comparing the abundance of SEQ ID NO. 16 from the sample tothe abundance of SEQ ID NO. 16 in a wild type sample.
 9. The process asrecited in claim 8 wherein, when the comparison is made, an upregulation in SEQ ID NO. 16 is found.
 10. The process as recited inclaim 9, wherein, when an up regulation is found, at least a 1.5 foldchange is found.
 11. The process as recited in claim 10, wherein SEQ IDNO. 16 is the only microRNA observed.
 12. The process as recited inclaim 8, wherein the step of observing includes the step of performing aquantitative polymerase chain reaction.